Articles

AbstractEthical issues in Language Resources and Language Technology are often invoked, but rarely discussed. This is at least partly because little work has been done to systematize ethical issues and principles applicable in the fields of Language Resources and Language Technology. This paper provides an overview of ethical issues that …...

ive Summaries Tim Fischer Steffen Remus Chris Biemann Language Technology Group Department of Informatics Universität Hamburg, Germany {firstname.lastname}@uni-hamburg.de Abstract Recent abstractive summarization systems fail to generate factually consistent – faithful – sum- maries, which heavily limits their practical ap- plication. Commonly, these models tend to mix concepts from the source …...

AbstractThe Linguistic Data Consortium was founded in 1992 to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development. At the time, DARPA had adopted the common task research management paradigm to impose additional rigor on their programs by also …...

AbstractProper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations. Since there exists no Dutch …...

AbstractQuestion answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much valued in chat-bots and corporate information systems. It is also a challenging task that attracted the attention of a very …...

AbstractIn this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article. We therefore propose to construct approaches that can …...

AbstractIn this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically …...

AbstractArgumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In …...

AbstractTo cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference …...

AbstractIn this paper, we present CrudeOilNews, a corpus of English Crude Oil news for event extraction. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining. This paper describes the data collection process, the annotation methodology, and …...

AbstractWe present VALET, a framework for rule-based information extraction written in Python. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information–lexical, orthographic, syntactic, corpus-analytic–in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement …...

AbstractLegal texts are often difficult to interpret, and people who interpret them need to make choices about the interpretation. To improve transparency, the interpretation of a legal text can be made explicit by formalising it. However, creating formalised representations of legal texts manually is quite labour-intensive. In this paper, we …...

AbstractIn recent years, natural language inference has been an emerging research area. In this paper, we present a novel data augmentation technique and combine it with a unique learning procedure for that task. Our so-called automatic contextual data augmentation (acda) method manages to be fully automatic, non-trivially contextual, and computationally …...

AbstractSkill Classification (SC) is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We release the first Danish job posting dataset: *Kompetencer* (_en_: competences), annotated for nested spans of competences. To improve upon coarse-grained annotations, we make …...

AbstractWe present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward—but rigorous—rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out …...

AbstractResearch suggests that using generic language models in specialized domains may be sub-optimal due to significant domain differences. As a result, various strategies for developing domain-specific language models have been proposed, including techniques for adapting an existing generic language model to the target domain, e.g. through various forms of vocabulary …...

AbstractKeyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided into ones that require training - supervised and ones that do not - unsupervised. In this study, …...

AbstractIn several ASR use cases, training and adaptation of domain-specific LMs can only rely on a small amount of manually verified text transcriptions and sometimes a limited amount of in-domain speech. Training of LSTM LMs in such limited data scenarios can benefit from alternate uncertain ASR hypotheses, as observed in …...

AbstractIn this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation has not been widely explored. To generate more human-like texts, we addressed disfluent text generation. The usage of disfluency, such as FPs, rephrases, and word …...

AbstractRunning large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly …...